Sampling Methods for Detection and Monitoring of the Asian Citrus Psyllid (Hemiptera: Psyllidae)
Derechos de accesoopenAccess
MetadatosMostrar el registro completo del ítem
AutorMonzo, C.; Arevalo, H. A.; Jones, M. M.; Vanaclocha, P.; Croxton, S. D.; Qureshi, J. A.; Stansly, P. A.
Cita bibliográficaMonzo, C., Arevalo, H.A., Jones, M.M., Vanaclocha, P., Croxton, S. D., Qureshi, J.A., Stansly, P. A. (2015). Sampling Methods for Detection and Monitoring of the Asian Citrus Psyllid (Hemiptera: Psyllidae). Environmental Entomology, 44(3), 780-788.
The Asian citrus psyllid (ACP), Diaphorina citri Kuwayama is a key pest of citrus due to its role as vector of citrus greening disease or "huanglongbing." ACP monitoring is considered an indispensable tool for management of vector and disease. In the present study, datasets collected between 2009 and 2013 from 245 citrus blocks were used to evaluate precision, sensitivity for detection, and efficiency of five sampling methods. The number of samples needed to reach a 0.25 standard error-mean ratio was estimated using Taylor's power law and used to compare precision among sampling methods. Comparison of detection sensitivity and time expenditure (cost) between stem-tap and other sampling methodologies conducted consecutively at the same location were also assessed. Stem-tap sampling was the most efficient sampling method when ACP densities were moderate to high and served as the basis for comparison with all other methods. Protocols that grouped trees near randomly selected locations across the block were more efficient than sampling trees at random across the block. Sweep net sampling was similar to stem-taps in number of captures per sampled unit, but less precise at any ACP density. Yellow sticky traps were 14 times more sensitive than stem-taps but much more time consuming and thus less efficient except at very low population densities. Visual sampling was efficient for detecting and monitoring ACP at low densities. Suction sampling was time consuming and taxing but the most sensitive of all methods for detection of sparse populations. This information can be used to optimize ACP monitoring efforts.